




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


snake_case_  :      List[str]		=		logging.get_logger(__name__)





class 					snake_case_   (     _lowerCAmelCase	):



				'''simple docstring'''
				lowerCamelCase       =  ['''pixel_values''']





				def __init__(      self							:   int      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   Optional[Dict[str, int]] = None      ,							__magic_name__							:   PILImageResampling = PILImageResampling.BILINEAR      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   Dict[str, int] = None      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   Union[int, float] = 1 / 255      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   Optional[Union[float, List[float]]] = None      ,							__magic_name__							:   Optional[Union[float, List[float]]] = None      ,							**__magic_name__							:   Any      ,							)							->			None:
											super().__init__(**UpperCAmelCase_							)
											lowerCamelCase_			:    Optional[Any]   						=      size if size is not None else {"shortest_edge": 256}
											lowerCamelCase_			:    str   						=      get_size_dict(UpperCAmelCase_      ,							default_to_square=UpperCAmelCase_							)
											lowerCamelCase_			:    int   						=      crop_size if crop_size is not None else {"height": 224, "width": 224}
											lowerCamelCase_			:    Tuple   						=      get_size_dict(UpperCAmelCase_      ,							param_name="crop_size"							)
											lowerCamelCase_			:    Optional[int]   						=      do_resize
											lowerCamelCase_			:    str   						=      size
											lowerCamelCase_			:    Dict   						=      resample
											lowerCamelCase_			:    Any   						=      do_center_crop
											lowerCamelCase_			:    Tuple   						=      crop_size
											lowerCamelCase_			:    Union[str, Any]   						=      do_rescale
											lowerCamelCase_			:    List[str]   						=      rescale_factor
											lowerCamelCase_			:    List[Any]   						=      do_normalize
											lowerCamelCase_			:    Optional[Any]   						=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											lowerCamelCase_			:    Union[str, Any]   						=      image_std if image_std is not None else IMAGENET_STANDARD_STD





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]      ,							__magic_name__							:   np.ndarray      ,							__magic_name__							:   Dict[str, int]      ,							__magic_name__							:   PILImageResampling = PILImageResampling.BICUBIC      ,							__magic_name__							:   Optional[Union[str, ChannelDimension]] = None      ,							**__magic_name__							:   Union[str, Any]      ,							)							->			np.ndarray:
											lowerCamelCase_			:    List[str]   						=      get_size_dict(UpperCAmelCase_      ,							default_to_square=UpperCAmelCase_							)
											if "shortest_edge" not in size:
																		raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"							)
											lowerCamelCase_			:    str   						=      get_resize_output_image_size(UpperCAmelCase_      ,							size=size["shortest_edge"]      ,							default_to_square=UpperCAmelCase_							)
											return resize(UpperCAmelCase_      ,							size=UpperCAmelCase_      ,							resample=UpperCAmelCase_      ,							data_format=UpperCAmelCase_      ,							**UpperCAmelCase_							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Union[str, Any]      ,							__magic_name__							:   np.ndarray      ,							__magic_name__							:   Dict[str, int]      ,							__magic_name__							:   Optional[Union[str, ChannelDimension]] = None      ,							**__magic_name__							:   str      ,							)							->			np.ndarray:
											lowerCamelCase_			:    List[str]   						=      get_size_dict(UpperCAmelCase_							)
											if "height" not in size or "width" not in size:
																		raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"							)
											return center_crop(UpperCAmelCase_      ,							size=(size["height"], size["width"])      ,							data_format=UpperCAmelCase_      ,							**UpperCAmelCase_							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Any      ,							__magic_name__							:   np.ndarray      ,							__magic_name__							:   float      ,							__magic_name__							:   Optional[Union[str, ChannelDimension]] = None      ,							**__magic_name__							:   Tuple							)							->			np.ndarray:
											return rescale(UpperCAmelCase_      ,							scale=UpperCAmelCase_      ,							data_format=UpperCAmelCase_      ,							**UpperCAmelCase_							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[Any]      ,							__magic_name__							:   np.ndarray      ,							__magic_name__							:   Union[float, List[float]]      ,							__magic_name__							:   Union[float, List[float]]      ,							__magic_name__							:   Optional[Union[str, ChannelDimension]] = None      ,							**__magic_name__							:   Any      ,							)							->			np.ndarray:
											return normalize(UpperCAmelCase_      ,							mean=UpperCAmelCase_      ,							std=UpperCAmelCase_      ,							data_format=UpperCAmelCase_      ,							**UpperCAmelCase_							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict      ,							__magic_name__							:   ImageInput      ,							__magic_name__							:   Optional[bool] = None      ,							__magic_name__							:   Dict[str, int] = None      ,							__magic_name__							:   PILImageResampling = None      ,							__magic_name__							:   bool = None      ,							__magic_name__							:   Dict[str, int] = None      ,							__magic_name__							:   Optional[bool] = None      ,							__magic_name__							:   Optional[float] = None      ,							__magic_name__							:   Optional[bool] = None      ,							__magic_name__							:   Optional[Union[float, List[float]]] = None      ,							__magic_name__							:   Optional[Union[float, List[float]]] = None      ,							__magic_name__							:   Optional[Union[str, TensorType]] = None      ,							__magic_name__							:   Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**__magic_name__							:   Tuple      ,							)							->			Dict:
											lowerCamelCase_			:    str   						=      do_resize if do_resize is not None else self.do_resize
											lowerCamelCase_			:    Union[str, Any]   						=      size if size is not None else self.size
											lowerCamelCase_			:    Any   						=      get_size_dict(UpperCAmelCase_      ,							default_to_square=UpperCAmelCase_							)
											lowerCamelCase_			:    Union[str, Any]   						=      resample if resample is not None else self.resample
											lowerCamelCase_			:    Any   						=      do_center_crop if do_center_crop is not None else self.do_center_crop
											lowerCamelCase_			:    str   						=      crop_size if crop_size is not None else self.crop_size
											lowerCamelCase_			:    str   						=      get_size_dict(UpperCAmelCase_      ,							param_name="crop_size"							)
											lowerCamelCase_			:    List[str]   						=      do_rescale if do_rescale is not None else self.do_rescale
											lowerCamelCase_			:    Any   						=      rescale_factor if rescale_factor is not None else self.rescale_factor
											lowerCamelCase_			:    Optional[int]   						=      do_normalize if do_normalize is not None else self.do_normalize
											lowerCamelCase_			:    Optional[Any]   						=      image_mean if image_mean is not None else self.image_mean
											lowerCamelCase_			:    Optional[int]   						=      image_std if image_std is not None else self.image_std

											lowerCamelCase_			:    Dict   						=      make_list_of_images(UpperCAmelCase_							)

											if not valid_images(UpperCAmelCase_							):
																		raise ValueError(
																		    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																		    "torch.Tensor, tf.Tensor or jax.ndarray."							)

											if do_resize and size is None:
																		raise ValueError("Size must be specified if do_resize is True."							)

											if do_center_crop and crop_size is None:
																		raise ValueError("Crop size must be specified if do_center_crop is True."							)

											if do_rescale and rescale_factor is None:
																		raise ValueError("Rescale factor must be specified if do_rescale is True."							)

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError("Image mean and std must be specified if do_normalize is True."							)

											# All transformations expect numpy arrays.
											lowerCamelCase_			:    List[Any]   						=      [to_numpy_array(UpperCAmelCase_							) for image in images]

											if do_resize:
																		lowerCamelCase_			:    int   						=      [self.resize(image=UpperCAmelCase_      ,							size=UpperCAmelCase_      ,							resample=UpperCAmelCase_							) for image in images]

											if do_center_crop:
																		lowerCamelCase_			:    Any   						=      [self.center_crop(image=UpperCAmelCase_      ,							size=UpperCAmelCase_							) for image in images]

											if do_rescale:
																		lowerCamelCase_			:    Optional[Any]   						=      [self.rescale(image=UpperCAmelCase_      ,							scale=UpperCAmelCase_							) for image in images]

											if do_normalize:
																		lowerCamelCase_			:    Any   						=      [self.normalize(image=UpperCAmelCase_      ,							mean=UpperCAmelCase_      ,							std=UpperCAmelCase_							) for image in images]

											lowerCamelCase_			:    Dict   						=      [to_channel_dimension_format(UpperCAmelCase_      ,							UpperCAmelCase_							) for image in images]

											lowerCamelCase_			:    Optional[int]   						=      {"pixel_values": images}
											return BatchFeature(data=UpperCAmelCase_      ,							tensor_type=UpperCAmelCase_							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict      ,							__magic_name__							:   List[str]      ,							__magic_name__							:   List[Tuple] = None							)							->			List[str]:
											lowerCamelCase_			:    int   						=      outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																		if len(UpperCAmelCase_							) != len(UpperCAmelCase_							):
																									raise ValueError(
																									    "Make sure that you pass in as many target sizes as the batch dimension of the logits"							)

																		if is_torch_tensor(UpperCAmelCase_							):
																									lowerCamelCase_			:    Dict   						=      target_sizes.numpy()

																		lowerCamelCase_			:    int   						=      []

																		for idx in range(len(UpperCAmelCase_							)							):
																									lowerCamelCase_			:    List[str]   						=      torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0							)      ,							size=target_sizes[idx]      ,							mode="bilinear"      ,							align_corners=UpperCAmelCase_							)
																									lowerCamelCase_			:    Dict   						=      resized_logits[0].argmax(dim=0							)
																									semantic_segmentation.append(UpperCAmelCase_							)
											else:
																		lowerCamelCase_			:    List[Any]   						=      logits.argmax(dim=1							)
																		lowerCamelCase_			:    List[str]   						=      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

											return semantic_segmentation



"""simple docstring"""


class 	__a	:






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		name
              UpperCamelCase       			=		value
              UpperCamelCase       			=		weight






       def __repr__(	self			:				int      )->	Any:







              """simple docstring"""
              return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Union[str, Any]:







              """simple docstring"""
              return self.value






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	int:







              """simple docstring"""
              return self.name






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[Any]:







              """simple docstring"""
              return self.weight






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Union[str, Any]:







              """simple docstring"""
              return self.value / self.weight





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		[]
       for i in range(len(UpperCAmelCase_					)					):
              menu.append(Things(name[i]		,   value[i]		,   weight[i]					)					)
       return menu



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		sorted(UpperCAmelCase_		,   key=UpperCAmelCase_		,   reverse=UpperCAmelCase_					)
       UpperCamelCase       			=		[]
       UpperCamelCase			,							UpperCamelCase       			=		0.0, 0.0
       for i in range(len(UpperCAmelCase_					)					):
              if (total_cost + items_copy[i].get_weight()) <= max_cost:
                     result.append(items_copy[i]					)
                     total_cost += items_copy[i].get_weight()
                     total_value += items_copy[i].get_value()
       return (result, total_value)



def 						lowerCamelCase__      (				)->				Dict:







       """simple docstring"""


if __name__ == "__main__":
      import doctest

      doctest.testmod()





